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ssuvetha
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Model Details
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ssuvetha
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TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
Input Modalities
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Supported Functionality
This repository contains a LoRA adapter trained on pharmaceutical domain text using QLoRA.
Base model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
This is non-instruction fine-tuning (continued pretraining / domain adaptation), not chat fine-tuning.
The model was trained as a causal language model on cleaned pharma-domain text extracted from a PDF corpus. Its objective is next-token prediction in the pharmaceutical domain.
This adapter is intended for:
python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torch base_model_id = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"adapter_id = "ssuvetha/pharma-tinyllama-domain-lora" tokenizer = AutoTokenizer.from_pretrained(adapter_id) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True,) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, device_map="auto",) model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torch base_model_id = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"adapter_id = "ssuvetha/pharma-tinyllama-domain-lora" tokenizer = AutoTokenizer.from_pretrained(adapter_id) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True,) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, device_map="auto",) model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()